This brief investigates the flexible leader–follower formation tracking problem for a group of nonholonomic mobile robots, while most of the formation control related work in the literature focuses on the rigid formation. The flexible formation discussed in this brief is defined in curvilinear coordinates in terms of longitudinal separations between robots along the reference trajectory and lateral deviations with respect to this trajectory. Unlike the previous studies on flexible formation control, this brief is under a more challenging assumption that the global position and orientation measurements are not available. To obtain the relative pose relationships amongst robots, a stereo camera is mounted on each follower. In consideration of the fact that visual observations are noise-corrupted and intermittently available, a particle filter-based relative pose estimation approach is employed to estimate the position and orientation of the leader in the local reference frame of the follower using the polluted and discontinuous information. Also, to form a flexible formation, the leader historical trajectory is reconstructed with respect to the current local frame attached on the follower, based on which a reference point is generated. In addition, this brief considers the situation where robots operate in unknown obstacle environments. To ensure robot safety in such environments, a multiobjective control law is proposed to balance reference tracking and collision avoidance in different situations. Simulation and real-robot experiment have been performed to demonstrate the efficacy of the proposed method.

中文翻译：

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未知障碍环境中多个非完整移动机器人的基于视觉的灵活领导者和跟随者追踪

本文简要研究了一组非完整的移动机器人的柔性跟随者跟随者编队跟踪问题，而文献中与编队控制有关的大多数工作都集中在刚性编队上。在此简要介绍的柔性结构是根据机器人之间沿参考轨迹的纵向间距以及相对于该轨迹的横向偏差的曲线坐标定义的。与先前有关柔性编队控制的研究不同，本简介是在一个更具挑战性的假设下进行的，即无法获得全球位置和方位测量值。为了获得机器人之间的相对姿势关系，在每个从动件上安装了一个立体摄像机。考虑到视觉观察结果已被噪声破坏并且可以间歇使用，基于粒子过滤器的相对姿势估计方法用于使用污染和不连续的信息来估计跟随者在本地跟随者参考帧中的位置和方向。同样，为了形成灵活的形式，相对于附着在跟随者上的当前局部框架来重建领导者历史轨迹，基于其生成参考点。此外，本摘要还考虑了机器人在未知障碍物环境中运行的情况。为了确保机器人在此类环境中的安全，提出了一种多目标控制律，以平衡不同情况下的参考跟踪和避免碰撞。仿真和真实机器人实验已经进行，以证明该方法的有效性。